The SSM Toolbox for Matlab

TL;DR

The SSM Toolbox for Matlab provides a comprehensive, interactive environment for time series analysis using state space models, supporting various model types, filtering, smoothing, and estimation techniques.

Contribution

It introduces a versatile MATLAB toolbox that simplifies the construction, analysis, and estimation of complex state space models for time series data.

Findings

Supports univariate and multivariate models

Includes Kalman filtering and smoothing functions

Provides model selection and decomposition tools

Abstract

State Space Models (SSM) is a MATLAB 7.0 software toolbox for doing time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dynamic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman filtering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with common analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for doing time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.